基于灰色连续区间Verhulst模型的大规模地震灾害救援药品需求预测研究

发布时间：2018-01-08 22:28

本文关键词：基于灰色连续区间Verhulst模型的大规模地震灾害救援药品需求预测研究　出处：《重庆工商大学》2016年硕士论文　论文类型：学位论文

【摘要】：大规模地震灾害发生后,其突发性和高破坏性常常造成重大的人员伤亡。国内外大规模地震灾害应急救援经验表明:在大规模地震灾害发生初期,如果能及时、准确的对地震灾区的伤病员进行有效的医疗救治,死亡人数将大大降低。然而,因救援时间紧迫、灾情信息有限、救援通道不足等不利因素的影响,当大规模地震灾害发生后,整个灾区对所有药品的种类和数量的需求不可能一次性全部送达,只能在考虑救援物资运输时间的基础上分批对物资进行调运,从而保障在有限条件下的物资调运最优。本文在此背景前提下,以国内外相关领域的研究成果为基础,运用灰色系统预测理论构建连续区间灰色模型对应急救援初期灾区救援药品需求量预测问题进行了研究,从而为大规模地震灾害救援初期充足的药品供给,医疗救助的效率和质量提供了保障。本文首先对灰色系统预测模型和大规模地震灾害应急救援物资预测相关文献进行查阅,总结国内外研究现状,发现研究中的不足之处并提出问题,构思本文的研究方法和主要内容。在此基础上对大规模地震灾害应急救援药品需求特性进行分析,发现直接对药品需求的种类和数量进行预测较为困难。因此本文采取先对伤病员人数进行预测,再依据伤病员人数与药品需求量之间的关系间接对药品需求量进行预测的方法。然后我们对伤病员人数的统计数据进行分析,发现大规模地震灾害中伤病员人数表现出饱和“S”形特征,并且每时每刻都在增加,是一个动态的连续变化的区间变量,体现出一定的规律性,可以构建模型进行预测。在以上分析的基础上,我们梳理出需要的理论基础,结合数据特点对区间灰数白化方法进行对比选择,分析经典灰色Verhulst模型的误差来源,选择灰色离散Verhulst模型,从而组合构建连续区间灰数(离散)Verhulst模型;再通过地震灾害实例统计数据进行验证,并用不同的数据和模型进行对比,给出误差检验;最后用组合构建的模型对大规模地震灾害应急救援药品需求量进行预测并对药品的供给保障提出建议,列出研究结论,指出研究中的不足之处,展望下阶段研究。
[Abstract]:After the large-scale earthquake disaster, its sudden and high destructive often cause heavy casualties. The experience of emergency rescue of large-scale earthquake disaster at home and abroad shows that: in the early stage of large-scale earthquake disaster, if it can be timely. Accurate medical treatment of the injured and sick in the earthquake-stricken area, the death toll will be greatly reduced. However, due to the emergency rescue time, limited disaster information, insufficient rescue channels and other adverse factors. After the large-scale earthquake disaster, the demand for all kinds and quantity of medicine in the whole disaster area can not be delivered all at one time, but can only be transferred in batches on the basis of considering the time of transportation of relief materials. In order to ensure the optimal transportation of goods under limited conditions, this paper based on the research results of related fields at home and abroad on the premise of this background. The grey system prediction theory is used to construct the continuous interval grey model to predict the demand of the relief drugs in the early stage of the emergency rescue, so as to provide sufficient medicine for the large-scale earthquake disaster rescue in the early stage. The efficiency and quality of medical assistance are guaranteed. Firstly, this paper reviews the grey system prediction model and the literature related to large-scale earthquake disaster emergency relief materials prediction, and summarizes the current research situation at home and abroad. Find the deficiency of the research and put forward the problem, conceive the research method and main content of this paper. On this basis, analyze the demand characteristics of the large-scale earthquake disaster emergency relief medicine. It is found that it is difficult to predict the types and quantity of drug demand directly. Then according to the relationship between the number of wounded and sick and the demand for drugs, we indirectly predict the demand for drugs. Then we analyze the statistical data of the number of the wounded and sick. It is found that the number of sick and wounded in large-scale earthquake disaster shows the characteristic of saturation "S" shape, and it is increasing at every moment. It is a dynamic and continuous variation interval variable, which shows certain regularity. On the basis of the above analysis, we sort out the needed theoretical basis, combining with the characteristics of the data, the whitening method of interval grey number can be compared and selected. The error source of the classical grey Verhulst model is analyzed and the grey discrete Verhulst model is selected to construct the continuous interval grey number (discrete Verhulst model). Then it is verified by the statistical data of earthquake disaster, and compared with different data and models, the error test is given. Finally, the combined model is used to predict the demand for emergency relief drugs for large-scale earthquake disasters, and give some suggestions on the supply and security of drugs, list the research conclusions, and point out the deficiencies of the research. Looking ahead to the next stage of the study.
【学位授予单位】：重庆工商大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：P315.9

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